Title page for ETD etd-06302003-122951

Type of Document

Master's Thesis

Author

Liebsch, Cindy Marie,

URN

etd-06302003-122951

Title

Simulation Input Modeling in the Absence of Data

Degree

Master of Science

Graduate Program

Co-Major: Industrial Engineering and Operations Research

Advisory Committee

Advisor Name	Title
Dr. David Kaber	Committee Co-Chair
Dr. Stephen Roberts	Committee Co-Chair
Dr. Jerome Lavelle	Committee Member
Dr. Reid Ness	Committee Member

Keywords

Input Model
Simulation

Date of Defense

2003-06-30

Availability

unrestricted

Abstract

Liebsch, Cindy Marie. Simulation Input Modeling in the Absence of Data. (Under the
direction of Dr. Stephen Roberts and Dr. David Kaber.)

Simulation models provide a powerful tool for analyzing real-world systems. These
models are driven by input data, so when inputs are unknown and no data exists, the
development of the simulation model becomes problematic. This research addresses the
problem of modeling inputs in the absence of data, with the goal being to define and
verify a formal group process for developing simulation model inputs when data is
lacking.

The recommended process modifies a Delphi process and employs a panel of subjectarea
experts to provide estimates through several rounds of web-based surveys. After
each round, the panelists? responses are analyzed, and a summary of the responses and
comments from the previous round, as well as any supplemental information, is provided
to the panelists to help them develop estimates in the next survey round. By sharing
information, the panelists gain insight into the beliefs and opinions of their colleagues,
resulting in a growing consensus about the questions addressed in the study.

This process was implemented in the Colorectal Cancer Simulation Study to develop
inputs for the simulation. As in this study and many other medical simulations, a
number of inputs are unknown or uncertain because appropriate data does not exist or
experiments cannot be performed to define the unknown inputs because of their grave
nature. In this study, fifteen experts from the areas of gastroenterology, epidemiology,
and microbiology were recruited to serve on the expert panel. Three rounds of webbased
surveys were conducted to reach consensus on four different study objectives
related to adenoma development and cancer progression. The final simulation model
inputs were developed using the estimates and the VisiFit distribution-fitting software.

To examine the flexibility, usefulness, and acceptability of the process, the expert
panelists and the study?s Advisory Board were sent evaluation surveys asking
specifically about the group process and the resulting inputs developed. The panelists
felt the process was flexible, required a minimal time commitment, and the web-based
surveys were easy to use. The group dynamics throughout the surveying process allowed
everyone to share information without worrying about dominance or groupthink. The
information available during the process to support estimate development was adequate
from the perspective of the panelists. Both the panelists and the Advisory Board found
the inputs developed via the process to be consistent with real-world cases of adenoma
development and cancer progression. They also believed the input estimates were more
accurate than what one individual or an informal group could have developed. Since the
group process was fully executed and a growing consensus of the estimates produced the
final simulation model inputs, the process was clearly feasible. The cost of the process
was easily justified because of the limited methods currently available to otherwise gain
this information for use in the Colorectal Cancer Simulation Study. Because this
information represents the best estimates available to date, and considering there is
limited data to support formal analysis, the inputs developed as a result of this process
are extremely valuable. The method developed was therefore deemed a success and
contributes a method for developing inputs in the absence of data to the field of
computer simulation modeling.

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